This type of methods is especially welcomed in the field of robotics [1]}, [2]} and autonomous driving [3]}. The critical idea behind Recovery RL is introducing a dedicated policy that recovers unsafe states, whereas the task policy is trained by the standard RL to achieve the original goal. However, those approaches struggle for a rational recovery policy since it tends to be overly conservative in preventing risky exploration. Furthermore, the decisions between two policies may conflict with each other, which makes the agents stuck near the boundaries of safe regions easily.
